Machine Learning based Pointing Models for Radio/Sub-millimeter Telescopes

TL;DR

This paper demonstrates that machine learning models, specifically XGBoost, can effectively reduce pointing errors in radio telescopes by learning from historical data, improving accuracy for better astronomical observations.

Contribution

The study introduces a machine learning approach to improve telescope pointing accuracy, outperforming traditional linear regression models in reducing residual errors.

Findings

XGBoost models reduced azimuth RMSE by 4.3%.

Elevation RMSE was reduced by 9.5%.

Results inform future telescope operations and design.

Abstract

Radio, sub-millimeter and millimeter ground-based telescopes are powerful instruments for studying the gas and dust-rich regions of the Universe that are invisible at optical wavelengths, but the pointing accuracy is crucial for obtaining high-quality data. Pointing errors are small deviations of the telescope's orientation from its desired direction. The telescopes use linear regression pointing models to correct for these errors, taking into account various factors such as weather conditions, telescope mechanical structure, and the target's position in the sky. However, residual pointing errors can still occur due to factors that are hard to model accurately, such as thermal and gravitational deformation and environmental conditions like humidity and wind. Here we present a proof-of-concept for reducing pointing error for the Atacama Pathfinder EXperiment (APEX) telescope in the high-altitude Atacama Desert in Chile based on machine learning. Using historic pointing data from 2022, we trained eXtreme Gradient Boosting (XGBoost) models that reduced the Root Mean Squared Error (RMSE) for azimuth and elevation (horizontal and vertical angle) pointing corrections by 4.3% and 9.5%, respectively, on hold-out test data. Our results will inform operations of current and future facilities such as the next-generation Atacama Large Aperture Submillimeter Telescope (AtLAST).